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Causal Inference

Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.

( Image credit: Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 991–1000 of 1722 papers

Title	Date	Tasks	Status	Hype
Estimating causal effects with optimization-based methods: A review and empirical comparison	Feb 28, 2022	Causal Inference	—Unverified	0
Off-Policy Evaluation with Policy-Dependent Optimization Response	Feb 25, 2022	Causal InferenceDecision Making	—Unverified	0
Causal discovery for observational sciences using supervised machine learning	Feb 25, 2022	BIG-bench Machine LearningCausal Discovery	CodeCode Available	0
Predicting the impact of treatments over time with uncertainty aware neural differential equations	Feb 24, 2022	Causal Inferencecounterfactual	CodeCode Available	0
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems	Feb 23, 2022	Causal InferenceDataset Generation	CodeCode Available	0
Partial Identification with Noisy Covariates: A Robust Optimization Approach	Feb 22, 2022	Causal Inference	—Unverified	0
Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data	Feb 22, 2022	Causal InferenceRepresentation Learning	—Unverified	0
Learning Causal Overhypotheses through Exploration in Children and Computational Models	Feb 21, 2022	Causal InferenceEfficient Exploration	—Unverified	0
A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics	Feb 18, 2022	Causal Inferenceparameter estimation	—Unverified	0
Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows	Feb 17, 2022	Causal Inferencecounterfactual	—Unverified	0

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All datasets IHDP Jobs IDHP

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Random Forest	Average Treatment Effect Error	0.96	—	Unverified
2	Balancing Linear Regression	Average Treatment Effect Error	0.93	—	Unverified
3	k-NN	Average Treatment Effect Error	0.79	—	Unverified
4	CEVAE	Average Treatment Effect Error	0.46	—	Unverified
5	Balancing Neural Network	Average Treatment Effect Error	0.42	—	Unverified
6	Causal Forest	Average Treatment Effect Error	0.4	—	Unverified
7	BCAUS DR	Average Treatment Effect Error	0.29	—	Unverified
8	TARNet	Average Treatment Effect Error	0.28	—	Unverified
9	Counterfactual Regression + WASS	Average Treatment Effect Error	0.27	—	Unverified
10	MTDL-KNN	Average Treatment Effect Error	0.23	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CFR WASS	Average Treatment Effect on the Treated Error	0.09	—	Unverified
2	CFR MMD	Average Treatment Effect on the Treated Error	0.08	—	Unverified
3	BART	Average Treatment Effect on the Treated Error	0.08	—	Unverified
4	GANITE	Average Treatment Effect on the Treated Error	0.06	—	Unverified
5	BCAUSS	Average Treatment Effect on the Treated Error	0.05	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BART	Average Treatment Effect Error	0.34	—	Unverified
2	OLS with separate regressors for each treatment	Average Treatment Effect Error	0.31	—	Unverified
3		Average Treatment Effect Error	-0.23	—	Unverified